#FHIR

I have challenged to create a bot application using Azure Bot that can retrieve and post data to IRIS for Health.

A patient's data has already been registered in the FHIR repository of IRIS for Health.

The patient's MRN is 1001. His name is Taro Yamada. (in Japanese :山田 太郎)

This bot can post new pulse oximeter readings as an observation resource linked to the patient.

Surely you have all heard about FHIR as the panacea and solution to all interoperability and compatibility problems between systems. Right here we can see one of his classic defenders holding a FHIR resource in his hand and enjoying it immensely:

But for the rest of us mortals we are going to make a small introduction.

New in version 2023.3 (of InterSystems IRIS for Health) is a capability to perform FHIR profile-based validation.

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In this article I'll provide a basic overview of this capability.

If FHIR is important to you, you should definitely try out this new feature, so read on.

Problem

In a fast-paced clinical environment, where quick decision-making is crucial, the lack of streamlined document storage and access systems poses several obstacles. While storage solutions for documents exist (e.g, FHIR), accessing and effectively searching for specific patient data within those documents meaningfully can be a significant challenge.

I created this application considering how to convert images such as prescription forms into FHIR messages

It recognizes the text in the image through OCR technology and extracts it, which is then transformed into fhir messages through AI (LLA language model).

Finally, sending the message to the fhir server of IntereSystems can verify whether the message meets the fhir requirements. If approved, it can be viewed on the select page.

Did you know that with InterSystems IRIS for Health™, you can create a FHIR Facade that enables healthcare apps to send and receive HL7® FHIR® data?
Find out how in this learning path (3h 30m), which includes a hands-on exercise (20m).

We resume our series of articles on the FHIR Adapter tool available to HealthShare HealthConnect and InterSystems IRIS users.

In the previous articles we have presented the small application on which we set up our workshop and showed the architecture deployed in our IRIS instance after installing the FHIR Adapter. In today's article we will see an example of how we can perform one of the most common CRUD (Create - Read - Update - Delete) operations, the reading operation, and we will do it by recovering a Resource.

In the last article we presented the architecture of our SMART On FHIR project, so it's time to get down to business and start configuring all the elements that we are going to need.

We will first start with Auth0.

AUTH0 configuration

We will start by creating an Auth0 account with a valid email, once registered we will have to create our first application, and we will do it from the menu on the left:

Hi everyone!

The other day I was reflecting on the possibilities that FHIR brings us in the world of interoperability and electronic medical records and it gave me mixed feelings, let me explain, first a small introduction.

Introduction

As you well know, FHIR is based on parameterizing a series of concepts that it calls resources in JSON or XML format with the intention of covering the most common cases within the healthcare environment, relying on HTTP communications to cover CRUD and query functionalities.

In the context of HL7 FHIR (Fast Healthcare Interoperability Resources), the terms "id" and "identifier" refer to specific elements used for identifying resources within the FHIR data model. For a newbie, these terms can be confusingly similar, but they serve distinct purposes.

Look at the below Patient resource for August T. Faulkner:

The resource has an id of “1” — generated by the FHIR server when the resource was created.
Patient August T. Faulkner also has a identifier (Medical Record Number) — possibly provided by the hospital — of 78510398960

Motivation

The motivation behind the InterLang project is rooted in the innovative integration of LangChain chatbot agents with the Fast Healthcare Interoperability Resources (FHIR) framework to revolutionize conversational social prescriptions in healthcare. This project aims to leverage the rich and standardized data available through FHIR, an emerging standard in healthcare data exchange, to inform and empower these advanced chatbot agents.

Processing FHIR resources with FHIR SQL BUILDER to predict the probability of developing hepatitis C disease

With the development of technology, the medical industry is also constantly advancing, and humans often pay more attention to their own health,
By learning and processing datasets through computers, diseases can be predicted.

Pre condition: Ability to use FHIR and ML
Firstly, our dataset is obtained from kaggle and transformed into FHIR resources based on patient gender, age, ALP or ALT, and imported into the FHIR resource repository

In the previous article we saw how we could recover a resource stored in the database of our particular HIS, so today we will see how we can add new records in our HIS whose origin is an FHIR resource that we receive in our system.